An Efficient ResNetSE Architecture for Smoking Activity Recognition from Smartwatch

Author:

Hnoohom Narit,Mekruksavanich Sakorn,Jitpattanakul Anuchit

Publisher

Computers, Materials and Continua (Tech Science Press)

Subject

Artificial Intelligence,Computational Theory and Mathematics,Theoretical Computer Science,Software

Reference41 articles.

1. A new approach for smoking event detection using a variational autoencoder and neural decision forest;Fan;IEEE Access,2020

2. Fast and robust deep convolutional neural networks for complex human activity recognition using smartphone;Qi;Sensors,2019

3. SmokeSense: Online activity recognition framework on smartwatches;Shoaib,2018

4. Resolving ambiguities in accelerometer data due to location of sensor on wrist in application to detection of smoking gesture;Cole,2017

5. Smoking activity recognition using a single wrist IMU and deep learning light;Añazco,2018

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4. Complexity-aware Features Selection for Wrist-worn Human Activity Recognition;2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops);2024-03-11

5. Deep Learning Networks for Complex Activity Recognition Based on Wrist-Worn Sensor;TENCON 2023 - 2023 IEEE Region 10 Conference (TENCON);2023-10-31

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